Analysis of Shanghai Composite Index Variation Based on Regression Analysis

Yujie CUI, Qipu XI, Junzhang HAO

Abstract


In this paper, through collecting data of Shanghai Composite Index since 2007, we analyze overall trend of the Shanghai stock market after the financial crisis, and carry on the forecast to the future trend in order to provide a meaningful guidance for people’s investment securities. Because the fitting results of simple regression is not good, we consider the long-term trend, seasonal fluctuations, cyclical fluctuations, irregular variables and other factors. We also add lagged variables and establish an ARIMA model through SPSS statistical analysis software. The fitting degree of model we built is good and the effect of prediction is significant improvement in the analysis.


Keywords


Shanghai composite index; Time series; ARIMA model

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References


Cryer, J. D., & Beasley, K. S. (2008). Time Series Analysis With Applications in R. Springer Texts in Statistics.

Dai, W. S., Shao,Y. J. E., & Lu, C. J. (2013). Incorporating feature selection method into support vector regression for stock index forecasting. Neural Computing & Applications, 23(6), 1551-1561.

Mezali, H., & Beasley, J. E. (2013). Quantile regression for index tracking and enhanced indexation. Journal of the Operational Research Society, 64(11), 1676-1692.




DOI: http://dx.doi.org/10.3968%2F4991

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